CONSCIOUSNESS AND DEPTH OF ANAESTHESIA ASSESSMENT BASED ON BAYESIAN ANALYSIS OF EEG SIGNALS.

MedLine Citation:

PMID:
23314762
Owner:
NLM
Status:
Publisher

Abstract/OtherAbstract:

This study applies Bayesian techniques to analyse EEG signals for the assessment of the consciousness and depth of anaesthesia. This method takes the limiting large-sample normal distribution as posterior inferences to implement the Bayesian paradigm. The Maximum a Posterior (MAP) is applied to de-noise the wavelet coefficients based on a shrinkage function. When the anaesthesia states change from awake to light, moderate and deep anaesthesia, the MAP values increase gradually. Based on these changes, a new function BDoA is designed to assess the depth of anaesthesia. The new proposed method is evaluated using anaesthetized EEG recordings and BIS data from 25 patients. The Bland- Alman plot is used to verify the agreement of BDoA and the popular BIS index. Correlation between BDoA and BIS was measured using prediction probability (Pk). In order to estimate the accuracy of DoA, the effect of sample n and variance ä on the Maximum Posterior Probability (MPP) is studied. The results show that the new index accurately estimates the patient's hypnotic states. Compared with the BIS index in some cases, BDoA index can estimate the patient¡¦s hypnotic state in the case of poor signal quality.